Two retirees. Same average return. Same starting balance. Same withdrawal amount. One runs out of money at 74. The other dies with twice what they started.
The difference? The order in which returns showed up.
This is sequence-of-returns risk - the single biggest threat to a retirement portfolio that most calculators completely ignore.
The Problem With Averages
Suppose your portfolio earns an average of 7% per year over 30 years. A simple calculator multiplies that forward and tells you everything is fine.
But "average 7%" can look like a thousand different paths. Maybe you earn 25% in year one and lose 15% in year three. Or maybe you lose 15% in year one and earn 25% in year three. The arithmetic average is the same. The ending balance is not.
When you are adding money (during your working years), a bad early stretch is actually helpful - you buy more shares at lower prices. But when you are withdrawing money, a bad early stretch is devastating. You sell shares at depressed prices to fund your spending, and those shares are gone forever. They cannot participate in the recovery.
This asymmetry is what makes retirement fundamentally different from accumulation.
A Concrete Example
Consider a portfolio of 1,000,000 with annual withdrawals of 40,000 (a classic 4% withdrawal rate). Two scenarios over 5 years:
Scenario A (bad years early): -15%, -10%, +8%, +18%, +22%
Scenario B (bad years late): +22%, +18%, +8%, -10%, -15%
Both scenarios have the same average annual return of 4.6%. But after 5 years of withdrawals:
- Scenario A balance: roughly 870,000
- Scenario B balance: roughly 1,020,000
That is a 150,000 gap from the same average return. Extend this over 25-30 years of retirement and the gap becomes the difference between financial security and portfolio depletion.
Why the First 5-10 Years Matter Most
Research consistently shows that returns in the first 5-10 years of retirement have an outsized impact on portfolio survival. By the time a decade has passed, the portfolio has either built enough of a buffer to withstand later downturns, or it has been permanently impaired by early losses combined with withdrawals.
This is why someone who retired in 1995 (ahead of a roaring bull market) had a completely different experience than someone who retired in 2000 (right before the dot-com crash), even though their 20-year average returns might look similar.
The retirement date itself becomes a source of risk. Not something you can control, not something you can predict.
How Monte Carlo Captures This
A Monte Carlo retirement calculator runs thousands of scenarios, each with a different randomly generated sequence of returns. Some sequences start with a crash. Some start with a boom. Some have the crash in the middle.
The result is not a single "you will be fine" or "you will not." It is a probability distribution. If 85% of the sequences lead to success, you know that 15% of plausible return orderings would deplete your portfolio. That 15% is sequence risk made visible.
A fixed-return calculator, by definition, tests exactly one sequence: the same return every single year. It has zero ability to reveal sequence risk.
How to Quantify Sequence Risk in Your Own Plan
Knowing that sequence risk exists is not enough. You need to measure how exposed your specific plan is. A Monte Carlo simulation is the only practical way to do this, because it generates thousands of different return orderings and shows you the full range of outcomes.
Here is what to look for in the results:
The gap between the median and the 5th percentile. The median outcome is the "typical" future. The 5th percentile is the outcome where the return sequence was bad - usually because large losses hit early. If your median portfolio at age 85 is 800,000 but the 5th percentile is 0, sequence risk is doing most of the damage. That gap is your vulnerability window made visible.
Success rate sensitivity to the first decade. Run two versions of the same plan: one with your full time horizon, one where you only look at outcomes where the first 10 years had below-average returns. If the success rate drops sharply in the second version, your plan is sequence-dependent. A robust plan holds up even when the early years are bad.
The iteration count matters here. Sequence risk lives in the tails of the distribution. At 1,000 iterations, you might only generate a handful of truly devastating early-loss sequences. At 10,000 or 50,000, you get a more complete picture of how bad the timing can get. If you care about the 5th percentile (and you should), you need enough iterations to populate it reliably.
Fat-tailed distributions make it worse. Under a normal distribution, a -30% first year is rare. Under a Student's t-distribution, it is merely uncommon. Since sequence risk is driven by extreme early losses, the distribution you choose for your simulation directly affects how much sequence risk it reveals. A Gaussian model will underestimate the probability of the worst sequences.
Historical Retirement Cohorts: Timing as Destiny
The data makes this concrete. Someone who retired in January 2000 with 1,000,000 and a 4% withdrawal rate walked straight into the dot-com crash and the 2008 financial crisis within their first decade. Even with the eventual recovery, the early withdrawals during those two drawdowns permanently impaired the portfolio.
Compare that to someone who retired in January 1982 - right at the start of one of the longest bull runs in U.S. history. Same savings, same withdrawal rate, completely different outcome. The 1982 retiree's portfolio likely doubled within 5 years. The early gains created a buffer that could absorb any later downturn.
The arithmetic average return across both periods was positive. The sequence of those returns determined whether the plan survived.
What You Can Actually Do About It
Sequence risk cannot be eliminated, but it can be managed:
Build a cash buffer. Keeping 1-2 years of spending in cash or short-term bonds means you do not have to sell equities during a downturn. You draw from the buffer instead and give your portfolio time to recover.
Use a flexible spending strategy. Rigid fixed withdrawals are the worst case for sequence risk. Strategies like Guyton-Klinger guardrails or floor-and-ceiling approaches reduce withdrawals in bad years and increase them in good years. This single change can improve portfolio survival rates by 10-20 percentage points.
Consider your asset allocation glide path. A slightly more conservative allocation in the first 5 years of retirement (the vulnerability window), shifting back toward equities later, can reduce the impact of early losses. This is sometimes called a "bond tent" or "rising equity glide path."
Test with realistic distributions. A Monte Carlo simulation that uses a normal distribution will underestimate the frequency of extreme early losses. One that uses fat-tailed distributions gives a more honest assessment of sequence risk - because extreme years are exactly the ones that do the most damage.
The Takeaway
Sequence-of-returns risk is not an edge case or a theoretical curiosity. It is the primary mechanism by which retirement plans fail. Two people with identical savings, identical spending, and identical long-run average returns can have opposite outcomes based purely on timing.
Any retirement projection that does not account for this - that gives you one number based on one assumed return - is not telling you what you need to know.